Image Segmentation
Transformers
TensorBoard
English
Instance_Segmentation
CPU_friendly
Transformers
rfdetr
supervision
roboflow
Eval Results (legacy)
Instructions to use Subh775/Seg-Basil-rfdetr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subh775/Seg-Basil-rfdetr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Subh775/Seg-Basil-rfdetr")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Subh775/Seg-Basil-rfdetr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"class_map": {"valid": [{"class": "Tulsi", "map@50:95": 0.9446961508421681, "map@50": 0.9756002616389032, "precision": 0.9800275482093664, "recall": 0.93}, {"class": "all", "map@50:95": 0.9446961508421681, "map@50": 0.9756002616389032, "precision": 0.9800275482093664, "recall": 0.93}], "test": [{"class": "Tulsi", "map@50:95": 0.9530843650602129, "map@50": 0.9907611404627783, "precision": 0.9804878048780488, "recall": 0.9500000000000001}, {"class": "all", "map@50:95": 0.9530843650602129, "map@50": 0.9907611404627783, "precision": 0.9804878048780488, "recall": 0.9500000000000001}]}, "map": 0.9756002616389032, "precision": 0.9800275482093664, "recall": 0.93} |